Ameru.ai

Ameru.ai is a new company focused on creating a future with no waste. Their goal is to change how businesses handle waste. They use smart, new products to help companies reach their goals for the environment, society, and good management. The main part of Ameru''s technology is the Smart Bin. This device uses AI to sort waste into four groups: plastic and metal, paper and cardboard, glass, and general trash. The smart bin uses computer vision to see and sort waste right away. This makes waste management easier and reduces the amount of recyclable materials that go to landfills.

Benefits
The Smart Bin has several important benefits. It makes waste management easier by sorting waste automatically. This means less need for people to sort waste by hand, saving time and work. The real-time sorting also makes sure that recyclable materials are separated correctly. This helps reduce waste sent to landfills. The Smart Bin also gives useful information about waste management. This helps businesses track their waste and work towards being more sustainable.

Use Cases
The Smart Bin is great for businesses that want to improve how they handle waste and help the environment. It can be used in offices, factories, and other places where a lot of waste is made. By using the Smart Bin, businesses can reduce waste, save money, and improve their efforts to be more sustainable. The Smart Bin''s real-time sorting and data collection features make it a useful tool for any organization that wants to manage waste better.

Additional Information
Ameru''s technology is supported by a full machine learning data engine. This engine has several parts: getting data, labeling data, training the model, putting the model to use, and improving the model. Getting data involves taking pictures of waste items using a Jetson Nano, an AI device. These pictures are used for real-time waste detection and sorting and are stored for more processing. The data is then sent to a cloud storage system for future model developments. Labeling data is important for training the machine learning model. Ameru uses a mix of manual and machine learning-assisted labeling with Label Studio, an open-source tool. This makes sure the model can correctly identify and sort waste items. The labeled data is then used to train a PyTorch machine learning model, which is put to use on the edge device for real-time waste classification. Model training is done using PyTorch, an open-source machine learning library. The ST6 team, which made the data engine for Ameru, used pre-trained image classification models from the EfficientNet family to start the implementation quickly. As the product needs changed, the team switched to using YOLO object detection models for their high accuracy and fast speed. Model training is done in the cloud, using high-performance computing from Lambda Labs, which offers flexible pricing for startups. After training, the models are put to use on the edge devices using the Jetson Nano. The models are changed to the ONNX format for better use and then made faster using TensorRT, a high-performance deep learning library from Nvidia. This makes the models faster and more efficient, which is important for real-time waste detection and sorting. Improving the model is an ongoing process that involves active learning. As new data is captured, incorrectly classified images are added to the training dataset, and the model is retrained to improve its accuracy and efficiency. The ST6 team uses a systematic process to check the model''s performance, find areas for improvement, and update the domain taxonomy as needed. Version control and experiment tracking tools, such as GitHub and Weights & Biases, are used to manage the model improvement process.